Heart Rate Variability analysis is based on measuring variations in length of intervals between consecutive R waves further referred to as RR intervals; it usually employs spectral or some other form of mathematical analysis for characterizing such variations. The clinical significance of heart rate variability analysis became apparent when decreased variability of heart rate was found to correlate with certain health abnormalities, e.g. diabetic autonomic neuropathy, myocardial dysfunction etc. (T. Bigger, N. Rottman. Spectral Analysis of RR Variability. Chapter 19 in Cardiac Arrhythmia—Mechanisms, Diagnosis, and Management, Podrid P J, Kowey P R editors. Baltimore: William & Wilkins, 1995: 280-298.) Further studies have established that the high frequency component correlates with the activity of the parasympathetic nervous system while the low frequency component can serve as a marker of both vagal and sympathetic modulation (“Heart rate variability. Standards of measurement, physiologic interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology”, European Heart Journal, 1996, 17: 354-381.)
The discovery of such markers of autonomic activity opened up new opportunities of physiologic assessment for a wide range of clinical applications. However, in order to make practical use of this important scientific discovery, it was necessary to solve the problem of deriving some form of quantitative relationship between sympathetic and parasympathetic activity from the spectral function. Mathematical analysis of heart rate variability usually generates multiple parameters, typically 20-30 parameters. Prior methods have typically used two or three parameters of spectral function, such as high frequency power, low frequency power, or ratio of low frequency to high frequency power, and used direct meanings of these parameters for identification of the activities of the parasympathetic nervous system (PSNS) and the sympathetic nervous system (SNS). The remaining parameters were typically not taken into consideration. Thus the problem of SNS-PSNS quantification, which has remained for many years the principal dilemma of HRV analysis, has been specifically in deriving two final parameters: SNS and PSNS, from all the multiple parameters obtained via mathematical analysis of heart rate variability (HRV).